Establishment and Optimization of College Students Physical Health Standard Test System Based on Neural Network Reliability Evaluation Model

At present, the physical condition of college students is declining day by day, and the relevant sports departments pay more and more attention to the daily physical exercise of college students. To deeply understand the physical health status of college students and further adjust the health intervention measures, this paper constructs a neural network reliability evaluation model and carries out the physical health standard test on 50 college students from the sports department of Nanchang University and analyzes the physical health test data by using the neural network error reverse evaluation method; to optimize the physical health test system, the accuracy of physical health test is fitted in parallel. Then, according to the fitting degree of the test results, the direction of health intervention is predicted, and some specific suggestions are made for college students’ health indicators such as running and vital capacity. The research shows that the fitting degree of the neural network reliability evaluation model is 86%, and the accuracy of the neural network model is high. In the 50 college students’ physical health project, the fitting degree of 50 m running and vital capacity is higher than that of long distance running. Therefore, the neural network reliability evaluation model is feasible for college students’ physical health tests and can rank the intervention items, which has great practical significance for the improvement of college students’ physical health.